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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI - Second Edition

By : Voron
4.2 (9)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.2 (9)
By: Voron

Overview of this book

Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.
Table of Contents (21 chapters)
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1
Part 1: Introduction to Python and FastAPI
7
Part 2: Building and Deploying a Complete Web Backend with FastAPI
13
Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

Implementing a Real-Time Object Detection System Using WebSockets with FastAPI

In the previous chapter, you learned how to create efficient REST API endpoints to make predictions with trained machine learning models. This approach covers a lot of use cases, given that we have a single observation we want to work on. In some cases, however, we may need to continuously perform predictions on a stream of input – for instance, an object detection system that works in real time with video input. This is exactly what we’ll build in this chapter. How? If you remember, besides HTTP endpoints, FastAPI also has the ability to handle WebSockets endpoints, which allow us to send and receive streams of data. In this case, the browser will send into the WebSocket a stream of images from the webcam, and our application will run an object detection algorithm and send back the coordinates and label of each detected object in the image. For this task, we’ll rely on Hugging Face...

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